Blog

Numbers on a Screen

by Preqin

  • 04 Jun 2019
  • PE
  • VC
  • HF
  • PD
  • RE
  • INF
  • NR

The final instalment of our blog series, taken from our Don’t Believe the Hype whitepaper, looks at the different ways data is used and analyzed.

Can You Repeat the Question?
Once all the available information has been gathered, and moulded to give a fair reflection of the realities of the industry, it may seem that the challenges are mostly over. But in fact, some of the hardest tasks are with how the raw data points can be most usefully drawn through into tools and analytical models that are of real help to the user.

One thing that data providers, and indeed companies of all stripes, sometimes forget is the actual tasks that their users are looking to achieve. Very few users will access a dataset by asking questions like “what is the median performance of a North America-based buyout fund with a 2004 vintage?” Instead, users might be asking “what’s going on in the healthcare private equity sector,” “what regions should I be paying most attention to in the next six months?” or “how is my portfolio doing compared to the market as a whole?”

This is to say that specific data points are needed for some use cases, while trends and patterns are more useful in others. Trying to guide users to the answers that they need is therefore a delicate balance between making the raw line-by-line dataset available and having aggregating tools that can automatically show them relevant trends or higher-level figures. The raw datasets can be overwhelming, but to apply too much built-in calculation is to force the user into accepting assumptions of which they may not even be aware.

Preqin strives to offer users the ability to employ both approaches. They can download the line-by-line unfiltered data themselves in order to apply their own models, or they can use our cutting-edge analysis tools to offer a quick overview and insight. But a binary choice does not reflect the needs of most users, so Preqin allows them to mix the two approaches: when making custom benchmarks or target lists, users can set aggregate parameters to find the funds, firms or institutions they want, and can then add or remove individual entrants according to their preferences. We believe that only this level of control allows users to really get to the heart of what they want to know without Preqin’s own biases obscuring the facts.

This matters because the data taken from providers is frequently used to plug into financial risk models, due diligence processes and decision-making analysis. Many of Preqin’s clients receive data through use of an API function, meaning that they will only ever see the data within their own client software. This means it is imperative to get the balance right between providing a clean dataset free from duplication or misclassification and providing an overworked dataset that has been previously modelled.

They Seek Them Here, They Seek Them There
The other key consideration is the connectivity of different datasets. What may be partitioned from a database perspective may well be part of a continuum of information useful to a user. Very few use cases are interested in only a single category of information. For most users, different data points will have bearing on other categories of data, forming several parts of a cohesive understanding of a sector or trend.

For instance, knowing which investors are targeting Sub-Saharan Africa naturally raises questions about how much money is going to the region. This in turns begs the questions as to which fund managers are raising it, what opportunities they are deploying it into and what returns they are seeing from it. Datasets that may be drawn from different collection methods, and curated by different teams, nonetheless need to be able to work seamlessly to provide a holistic reflection of what is going on.

Likewise, the decisions that an investor makes about where to target for their next private equity investment will have a tangible impact on whether, when and where they invest in real estate, hedge funds or other asset classes. Preqin believes that keeping data siloed between different asset classes and data types is not an effective way for users to gain intelligence on the market, which is why we have developed at-a-glance profiles and searches that span all regions and asset classes.

Find out more about Preqin’s data collection process or request a demo to view our product to see why our data is the best in the industry.

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